Sequential Labeling-Assisted Precise and Multitarget Analysis of Surface Proteins on Extracellular Vesicles

Analysis of multiple surface proteins on extracellular vesicles (EVs) can reveal biological characteristics and potential therapeutic targets of cancer, particularly in highly heterogeneous breast cancer. However, due to the limited surface area of EVs, spatial hindrance remains a challenge for multiprotein assessment. Here, we present a sequential labeling-assisted electrochemical method for the precise and multitarget analysis of surface proteins on EVs, using breast cancer-related epidermal growth factor receptor and programmed death ligand-1 as examples. This sequential labeling is achieved through the use of a pair of aptamer probes functionalized with electroactive nanoparticles and an oxidative cleavage process facilitated by the bleomycin-Fe<sup>2+</sup> complex. The results demonstrate that sequential labeling efficiently avoids the adverse effects of spatial hindrance, enabling accurate analysis of target surface proteins on as low as 341 particles/mL of standard EVs derived from triple-negative breast cancer (TNBC) cells. Moreover, this sequential labeling-assisted method is successfully applied to clinical blood samples from healthy individuals and TNBC patients, highlighting its potential utility in early diagnosis and disease-course monitoring of breast cancer. Therefore, this work offers a feasible tool for the precise identification and analysis of multiple surface proteins on individual EVs, providing valuable information at the protein level for the accurate diagnosis and personalized treatment of breast cancer.

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Publication Details

Journal
Analytical Chemistry
Published
2025-05-19
DOI
https://doi.org/10.1021/acs.analchem.5c00326
Citations
3
Primary Topic
Extracellular vesicles in disease
Type
article
Field-Weighted Citation Impact
2.42

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Sequential Labeling-Assisted Precise and Multitarget Analysis of Surface Proteins on Extracellular Vesicles

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article

Sequential Labeling-Assisted Precise and Multitarget Analysis of Surface Proteins on Extracellular Vesicles

Jing Zhao, Ya Cao, Jie Yang, Zihan Zou, Xiaomeng Yu, Kai Zhang, Jianan Xia, Zhaoyin Wang
article en
3 citations

Abstract

Analysis of multiple surface proteins on extracellular vesicles (EVs) can reveal biological characteristics and potential therapeutic targets of cancer, particularly in highly heterogeneous breast cancer. However, due to the limited surface area of EVs, spatial hindrance remains a challenge for multiprotein assessment. Here, we present a sequential labeling-assisted electrochemical method for the precise and multitarget analysis of surface proteins on EVs, using breast cancer-related epidermal growth factor receptor and programmed death ligand-1 as examples. This sequential labeling is achieved through the use of a pair of aptamer probes functionalized with electroactive nanoparticles and an oxidative cleavage process facilitated by the bleomycin-Fe<sup>2+</sup> complex. The results demonstrate that sequential labeling efficiently avoids the adverse effects of spatial hindrance, enabling accurate analysis of target surface proteins on as low as 341 particles/mL of standard EVs derived from triple-negative breast cancer (TNBC) cells. Moreover, this sequential labeling-assisted method is successfully applied to clinical blood samples from healthy individuals and TNBC patients, highlighting its potential utility in early diagnosis and disease-course monitoring of breast cancer. Therefore, this work offers a feasible tool for the precise identification and analysis of multiple surface proteins on individual EVs, providing valuable information at the protein level for the accurate diagnosis and personalized treatment of breast cancer.

Analytical ChemistryVol. 97(21)
Shanghai University (CN), Nanjing Normal University (CN), Wenzhou Medical University (CN), Dongyang People's Hospital (CN), Nanjing University (CN)
Natural Science Foundation of Shanghai, National Natural Science Foundation of China, State Key Laboratory of Analytical Chemistry for Life Sciences
Zero hunger
Openalex Percentile: Top 12%
Extracellular vesicles in disease
2.42
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